Demand Modeling

Code:

2ME33

Acronym:

DM

Keywords
Classification	Keyword
OFICIAL	Economics

Instance: 2023/2024 - 2S

Active?	Yes
Course/CS Responsible:	Master in Economics

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
ME	10	Official Syllabus after 2021-2022	1	-	3	21	81
MEEE	16	Syllabus	1	-	3	21	81

Teaching language

English

Objectives

The course aims to introduce economic modeling of the discrete demand choices function, its estimation and use in forecasting and as an
instrument of economic policy.
In this context, the main objectives of the course are:
1. Characterize the alternative ways of modeling the demand for
discrete elements identifying the underlying economic theory
2. Know the components and the basic principles to design a customer survey of stated preferences, based on the design of statistical experiments (experimental design)
3. Know the different econometric procedures to estimate discrete choice models with stated preferences data, revealed preferences data
and aggregated data
4. Identify the multiple applications of the addressed methodologies and their framework in economic theory

Learning outcomes and competences

At the end of the couse, the student should be able to:
1. Know the main discrete choice models and their properties and identify the multiple situations of its applicability.
2. Be able to apply discrete choice modeling techniques identifying the main required data. Develop a survey in a web environment that allows to collect stated preference data for a given discrete choice model. Estimate the model and interpret the results.
3. Use the studied models to implement pricing strategies, product segmentation market and forecast demand. Characterize the effects of these same strategies on consumers' well-being.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Econometrics at the introductory level.

Program

1. Theoretical foundations and behavioral models
2. Binary and multinomial choice models: Probit and Logit
3. Introduction to stated choice survey design
4. Specification and estimation of discrete choice models with stated preferences and revealed preferences data
5. Econometric tests of the models: IIA
6. Generalizations in the logit model and its properties: Nested Logit, generalized extreme value models (GEV), logit mixing models
7. Applications: Welfare measurement, pricing strategies, market segmentation, demand forecasting

Mandatory literature

Train, Kenneth; Discrete choice methods with simulation, Cambridge University Press, 2009
Louviere, J. J., Hensher, D. A., & Swait, J. D.; Stated choice methods: analysis and applications., Cambridge University Press, 2000

Teaching methods and learning activities

Combination of theoretical and practical classes. In addition to exposing theoretical models and solving practical exercises, students must prepare and implement a survey in a web environment that allows estimating the models covered.

Software

R
Stata

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Teste	50,00
Trabalho prático ou de projeto	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Apresentação/discussão de um trabalho científico	1,00
Elaboração de projeto	59,00
Frequência das aulas	21,00
Total:	81,00

Eligibility for exams

Work project and a written test are mandatory to obtain approval.

Calculation formula of final grade

The final grade is computed as follows:

0.5*Work Project + 0.5* Test